3 个仓库
Systems that transform source code and documentation into unified context for AI agents.
Distinct from Context Provider Frameworks: Distinct from general context provider frameworks by specializing in the transformation of Git repositories into agent context.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Codebase Context Providers. Refine with filters or upvote what's useful.
git-mcp is a Model Context Protocol server that transforms Git repositories and static sites into structured context providers for AI assistants. It functions as a documentation retrieval tool and repository indexer, exposing codebases and project files as standardized tools to reduce hallucinations in large language model responses. The project converts raw repository files, READMEs, and external URLs into formats optimized for token consumption. It enables AI agents to perform query-based code searches and retrieve specific sections of project documentation to maintain up-to-date technical
Transforms codebases and static sites into structured context to reduce hallucinations in LLM responses.
Claude-context is a retrieval-augmented generation pipeline and semantic code search tool. It functions as an LLM codebase indexer and RAG context provider, designed to index local directories and retrieve relevant code files to provide context for large language models. The system operates as a hybrid search engine that combines keyword matching with dense vector search. This allows for the retrieval of code snippets and logic using natural language queries based on meaning rather than exact text matches. The project covers codebase indexing and search index management, utilizing asynchrono
Transforms source code and documentation into a unified, filtered context for use in LLM prompts.
OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a
Transforms indexed codebase information and long-term memory into a unified development context for AI agents.